NOWCASTING PREDICTION OF SOLAR IRRADIANCE BASED ON FY-4A AND MACHINE LEARNING

Jia Dongyu; Li Kaiming; Gao Xiaoqing; Gao Yumeng

doi:10.19912/j.0254-0096.tynxb.2022-1972

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (4) : 578-583. DOI: 10.19912/j.0254-0096.tynxb.2022-1972

NOWCASTING PREDICTION OF SOLAR IRRADIANCE BASED ON FY-4A AND MACHINE LEARNING

Jia Dongyu¹, Li Kaiming¹, Gao Xiaoqing², Gao Yumeng³

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Abstract

In view of the characteristics of abundant radiation resources but lack of observation data in China, this study proposes a short-term solar irradiance forecasting method based on radiation observation data, remote sensing data, McClear, and random forest algorithm, and focuses on analyzing the impact of remote sensing data on radiation forecasting effectiveness. The results show that adding remote sensing data can optimize the forecasting effectiveness at different time horizons and significantly reduce the probability of large prediction errors with a mean absolute percentage error （MAPE） value exceeding 40%. Additionally, the improvement of the forecasting effectiveness with the addition of remote sensing data increases linearly with the time horizon. The difference range of normalized root mean square error （nRMSE） changes from 2.08% to 13.81%, the difference of normalized mean absolute error （nMAE） changes from 1.64% to 14.52%, the difference of R² shows the most significant change with the time step, changing from -0.03 to -0.43. However, it is worth noting that adding satellite data will significantly increase the time required for model establishment and hyperparameter optimization.

Key words

solar irradiance / forecasting / machine learning / FY-4A / clear sky model

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Jia Dongyu, Li Kaiming, Gao Xiaoqing, Gao Yumeng. NOWCASTING PREDICTION OF SOLAR IRRADIANCE BASED ON FY-4A AND MACHINE LEARNING[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 578-583 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1972

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